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Stevenson DS. Modelling the structure and evolution of cultural information as quasispecies. Biosystems 2024; 235:105104. [PMID: 38128874 DOI: 10.1016/j.biosystems.2023.105104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023]
Abstract
We present a novel mode of cultural evolution whereby some forms of transmission may be modelled as quasispecies. The model incorporates the effect of high rates of error in certain forms of communication; while also building on the structural similarities between biological molecules and written language. Firstly, both written language and key biological molecules, such as RNA and proteins, are modular. Within these molecules, structural domains may be recombined, while retaining their function. Likewise, sentences are structured as combinations of clauses, in which each clause contains a domain of information. The clausal structure permits the recombination of information to adopt different meanings, while allowing each unit to retain its identity. Secondly, by virtue of intrinsically-high error rates, we show that some, but not all, aspects of communicated culture information exists as rapidly evolving clouds within the population. These clouds of cultural information behave as quasispecies, which we model with varying mutation rates and suitable selection coefficients. We then integrate these ideas with the application of Shannon Diversity Index to produce a more holistic view of culture that is centred on the evolution of its information. Re-imagining culture, as evolving clouds of information, unifies the mode in which information is stored culturally and biologically, and opens up new avenues of comparative analysis.
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Burchardt LS, Knörnschild M. Comparison of methods for rhythm analysis of complex animals' acoustic signals. PLoS Comput Biol 2020; 16:e1007755. [PMID: 32267836 PMCID: PMC7141653 DOI: 10.1371/journal.pcbi.1007755] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 02/28/2020] [Indexed: 12/21/2022] Open
Abstract
Analyzing the rhythm of animals' acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data.
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Affiliation(s)
- Lara S. Burchardt
- Museum für Naturkunde, Invalidenstraße, Berlin, Germany
- Animal Behavior Lab, Free University Berlin, Berlin, Germany
| | - Mirjam Knörnschild
- Museum für Naturkunde, Invalidenstraße, Berlin, Germany
- Animal Behavior Lab, Free University Berlin, Berlin, Germany
- Smithsonian Tropical Research Institute, Barro Colorado Island, Balboa, Ancón, Panamá
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Ravignani A, Verga L, Greenfield MD. Interactive rhythms across species: the evolutionary biology of animal chorusing and turn-taking. Ann N Y Acad Sci 2019; 1453:12-21. [PMID: 31515817 PMCID: PMC6790674 DOI: 10.1111/nyas.14230] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 08/13/2019] [Accepted: 08/14/2019] [Indexed: 12/11/2022]
Abstract
The study of human language is progressively moving toward comparative and interactive frameworks, extending the concept of turn-taking to animal communication. While such an endeavor will help us understand the interactive origins of language, any theoretical account for cross-species turn-taking should consider three key points. First, animal turn-taking must incorporate biological studies on animal chorusing, namely how different species coordinate their signals over time. Second, while concepts employed in human communication and turn-taking, such as intentionality, are still debated in animal behavior, lower level mechanisms with clear neurobiological bases can explain much of animal interactive behavior. Third, social behavior, interactivity, and cooperation can be orthogonal, and the alternation of animal signals need not be cooperative. Considering turn-taking a subset of chorusing in the rhythmic dimension may avoid overinterpretation and enhance the comparability of future empirical work.
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Affiliation(s)
- Andrea Ravignani
- Artificial Intelligence LabVrije Universiteit BrusselBrusselsBelgium
- Institute for Advanced StudyUniversity of AmsterdamAmsterdamthe Netherlands
- Research DepartmentSealcentre PieterburenPieterburenthe Netherlands
| | - Laura Verga
- Faculty of Psychology and Neuroscience, Department NP&PPMaastricht UniversityMaastrichtthe Netherlands
| | - Michael D. Greenfield
- Department of Ecology and Evolutionary BiologyUniversity of KansasLawrenceKansas
- Equipe Neuro‐Ethologie Sensorielle, ENES/Neuro‐PSI, CNRS UMR 9197Université de Lyon/Saint‐EtienneSaint EtienneFrance
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Burchardt LS, Norton P, Behr O, Scharff C, Knörnschild M. General isochronous rhythm in echolocation calls and social vocalizations of the bat Saccopteryx bilineata. ROYAL SOCIETY OPEN SCIENCE 2019; 6:181076. [PMID: 30800360 PMCID: PMC6366212 DOI: 10.1098/rsos.181076] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 11/29/2018] [Indexed: 05/05/2023]
Abstract
Rhythm is an essential component of human speech and music but very little is known about its evolutionary origin and its distribution in animal vocalizations. We found a regular rhythm in three multisyllabic vocalization types (echolocation call sequences, male territorial songs and pup isolation calls) of the neotropical bat Saccopteryx bilineata. The intervals between element onsets were used to fit the rhythm for each individual. For echolocation call sequences, we expected rhythm frequencies around 6-24 Hz, corresponding to the wingbeat in S. bilineata which is strongly coupled to echolocation calls during flight. Surprisingly, we found rhythm frequencies between 6 and 24 Hz not only for echolocation sequences but also for social vocalizations, e.g. male territorial songs and pup isolation calls, which were emitted while bats were stationary. Fourier analysis of element onsets confirmed an isochronous rhythm across individuals and vocalization types. We speculate that attentional tuning to the rhythms of echolocation calls on the receivers' side might make the production of equally steady rhythmic social vocalizations beneficial.
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Affiliation(s)
- Lara S. Burchardt
- Institute of Animal Behavior, Freie Universität Berlin, Takustr. 6, 14195 Berlin, Germany
- Author for correspondence: Lara S. Burchardt e-mail:
| | - Philipp Norton
- Institute of Animal Behavior, Freie Universität Berlin, Takustr. 6, 14195 Berlin, Germany
| | - Oliver Behr
- University of Erlangen-Nuremberg, Paul-Gordan-Str. 3/5, 91052 Erlangen, Germany
| | - Constance Scharff
- Institute of Animal Behavior, Freie Universität Berlin, Takustr. 6, 14195 Berlin, Germany
| | - Mirjam Knörnschild
- Institute of Animal Behavior, Freie Universität Berlin, Takustr. 6, 14195 Berlin, Germany
- Smithsonian Tropical Research Institute, Barro Colorado Island, Roosevelt Avenue, Tupper Building – 401, Balboa, Ancón, Panamá
- Museum für Naturkunde – Leibniz Institute for Evolution and Biodiversity Science, Invalidenstraße 43, 10115 Berlin, Germany
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Asano R, Seifert U. Commentary: The Evolution of Musicality: What Can Be Learned from Language Evolution Research? Front Neurosci 2018; 12:640. [PMID: 30283293 PMCID: PMC6156452 DOI: 10.3389/fnins.2018.00640] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 08/28/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Rie Asano
- Systematic Musicology, Institute of Musicology, University of Cologne, Cologne, Germany
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Ravignani A, Thompson B, Grossi T, Delgado T, Kirby S. Evolving building blocks of rhythm: how human cognition creates music via cultural transmission. Ann N Y Acad Sci 2018; 1423:176-187. [PMID: 29508405 DOI: 10.1111/nyas.13610] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 12/25/2017] [Accepted: 12/31/2017] [Indexed: 01/01/2023]
Abstract
Why does musical rhythm have the structure it does? Musical rhythm, in all its cross-cultural diversity, exhibits commonalities across world cultures. Traditionally, music research has been split into two fields. Some scientists focused on musicality, namely the human biocognitive predispositions for music, with an emphasis on cross-cultural similarities. Other scholars investigated music, seen as a cultural product, focusing on the variation in world musical cultures. Recent experiments found deep connections between music and musicality, reconciling these opposing views. Here, we address the question of how individual cognitive biases affect the process of cultural evolution of music. Data from two experiments are analyzed using two complementary techniques. In the experiments, participants hear drumming patterns and imitate them. These patterns are then given to the same or another participant to imitate. The structure of these initially random patterns is tracked along experimental "generations." Frequentist statistics show how participants' biases are amplified by cultural transmission, making drumming patterns more structured. Structure is achieved faster in transmission within rather than between participants. A Bayesian model approximates the motif structures participants learned and created. Our data and models suggest that individual biases for musicality may shape the cultural transmission of musical rhythm.
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Affiliation(s)
- Andrea Ravignani
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Language and Cognition Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
- Research Department, Sealcentre Pieterburen, Pieterburen, the Netherlands
| | - Bill Thompson
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Language and Cognition Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Thomas Grossi
- Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Tania Delgado
- Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
- Department of Cognitive Science, University of California, San Diego, La Jolla, California
| | - Simon Kirby
- Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
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